An optimization framework for flexible inter-tenant resource
sharing with transmission power control is proposed in [1]} to improve network capacity and the utilization of base station resources, where interference levels are controllable. However, achievable rates of channels are assumed to be deterministic, which is an impractical assumption as the achievable rates are random in wireless channels [2]}, [3]}, [4]}. Network slicing algorithms for slice recovery and reconfiguration
under stochastic demands in service-oriented networks are studied in [5]}. Wang et al. use \(\ell _1\) -norm to promote sparsity in slice reconfigurations in [6]}. To tackle the non-differentiability of \(\ell _1\) -norm, affine constraints that allow limited slice variations are considered instead of \(\ell _1\) -norm in [6]}. The statistics of the demand are not used in [6]} for network slicing. In [9]}, Pozza et al. propose a heuristic divide-and-conquer approach for finding a sequence of feasible
solutions to reconfigure a slice made of a chain of network functions under bandwidth and latency constraints, while RAN is not considered in the formulations.
To tackle the high computational complexity caused by the large number of variables in the slice reconfiguration problem, a heuristic depth-first-search algorithm is proposed in [10]} to find a set of possible reconfigurations, and a reinforcement learning approach is used to explore the multi-dimensional discrete action space. An end-to-end network slicing method for 5G networks without imposing constraints on available resources is proposed in [11]}, where the slice status is monitored and necessarily reconfigured in an online fashion via a heuristic approach. However, reconfiguring end-to-end slices based on instantaneous demands significantly increases the reconfiguration costs.
